Development and Patient-Level Internal Validation of an MRI-Based Radiomics Machine Learning Model for Preoperative Prediction of Cervical Lymph Node Metastasis in Oral Squamous Cell Carcinoma: A Pilot Study

Background/Objectives: Cervical lymph node metastasis (LNM) is the main prognostic factor in oral squamous cell carcinoma (OSCC) and guides the extent of neck treatment. This retrospective pilot study aimed to develop MRI-based radiomics machine learning models for the preoperative prediction of LNM in OSCC and to validate them internally at the patient level.

Methods

Preoperative MRI of 74 patients (32 LNM-positive, 42 LNM-negative on histopathology) was analysed. In each patient the largest morphologically suspicious cervical lymph node was segmented volumetrically on axial contrast-enhanced T1-weighted images, and 1409 IBSI-compliant radiomic features were extracted. Performance was estimated by 10 repeats of 5-fold nested cross-validation with the patient as the unit of partitioning; standardisation, correlation pruning, LASSO selection, hyperparameter tuning and threshold choice were confined to the training folds. Six classifiers were compared.